Neural Network User Authentication by Geometry of the Auricle

2018 ◽  
pp. 11-19 ◽  
Author(s):  
Berik Akhmetov ◽  
Ihor Tereikovskyi ◽  
Liudmyla Tereikovska ◽  
Asselkhan Adranova
Keyword(s):  
Neural Network ◽  
User Authentication

Earprint Based Mobile User Authentication Using Convolutional Neural Network and SIFT

2018 ◽  
pp. 874-880
Author(s):  
Mudit Maheshwari ◽  
Sanchita Arora ◽  
Akhilesh M. Srivastava ◽  
Aditi Agrawal ◽  
Mahak Garg ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
User Authentication ◽  
Mobile User

Continuous User Authentication Based on Keystroke Dynamics through Neural Network Committee Machines

2011 ◽  
pp. 232-252
Author(s):  
Sérgio Roberto de Lima e Silva Filho ◽  
Mauro Roisenberg
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
User Authentication ◽  
Keystroke Dynamics ◽  
Continuous Authentication ◽  
Biometric Characteristic ◽  
Continuous User ◽  
Computer Keyboard

This chapter proposes an authentication methodology that is both inexpensive and non-intrusive and authenticates users continuously while using a computer keyboard. This proposed methodology uses neural network committee machines. The committee consists of several independent neural networks trained to recognize a behavioral biometric characteristic: user’s typing pattern. Continuous authentication prevents potential attacks when users leave their desks without logging out or locking their computer session. Some experiments were conducted to evaluate and to calibrate the authentication committee. Best results show that a 0% FAR and a 0.15% FRR can be achieved when different thresholds are used in the system for each user. In this proposed methodology, capture system does not need to concern about typing errors in the text. Another feature of this methodology is that new users can be easily added to the system, with no need to re-train all neural networks involved.

scholarly journals Secure Fingerprint Authentication Using Deep Learning and Minutiae Verification

2019 ◽  
Vol 29 (1) ◽  
pp. 1379-1387 ◽  
Author(s):  
V.M. Praseetha ◽  
Saad Bayezeed ◽  
S. Vadivel
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
User Authentication ◽  
Optical Image ◽  
Deep Convolutional Neural Network ◽  
False Identification ◽  
Fingerprint Verification ◽  
Fingerprint Biometrics ◽  
The Given

Abstract Nowadays, there has been an increase in security concerns regarding fingerprint biometrics. This problem arises due to technological advancements in bypassing and hacking methodologies. This has sparked the need for a more secure platform for identification. In this paper, we have used a deep Convolutional Neural Network as a pre-verification filter to filter out bad or malicious fingerprints. As deep learning allows the system to be more accurate at detecting and reducing false identification by training itself again and again with test samples, the proposed method improves the security and accuracy by multiple folds. The implementation of a novel secure fingerprint verification platform that takes the optical image of a fingerprint as input is explained in this paper. The given input is pre-verified using Google’s pre-trained inception model for deep learning applications, and then passed through a minutia-based algorithm for user authentication. Then, the results are compared with existing models.

scholarly journals Fingerprint Presentation Attack Detection using Referential Quality Metrics and Minutiae Count

2021 ◽  
Author(s):  
Akhilesh Verma ◽  
Anshadha Gupta ◽  
Mohammad Akbar ◽  
Arun Kumar Yadav ◽  
Divakar Yadav
Keyword(s):  
Neural Network ◽  
Error Rate ◽  
State Of The Art ◽  
User Authentication ◽  
Attack Detection ◽  
Classification Error ◽  
The Public ◽  
The Past ◽  
Biometric Systems ◽  
Presentation Attack Detection

Abstract The fingerprint presentation attack is still a major challenge in biometric systems due to its increased applications worldwide. In the past, researchers used Fingerprint Presentation Attack Detection (FPAD) for user authentication, but it suffers from reliable authentication due to less focus on reducing the ‘error rate’. In this paper, we proposed an algorithm, based on referential image quality (RIQ)-metrics and minutiae count using neural network, k-NN and SVM for FPAD. We evaluate and validate the error rate reduction with different machine learning models on the public domain, such as LivDet crossmatch dataset2015 and achieved an accuracy of 88% with a neural network, 88.6% with k-NN and 88.8% using SVM. In addition, the average classification error (ACE) score is 0.1197 for ANN, 0.1138 for k-NN and 0.1117 for SVM. Thus, the results obtained show that it was achieved a reasonable accuracy with a low ACE score with respect to other state-of-the-art methods.

Continuous User Authentication Based on Keystroke Dynamics through Neural Network Committee Machines

2013 ◽  
pp. 410-429
Author(s):  
Sérgio Roberto de Lima e Silva Filho ◽  
Mauro Roisenberg
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
User Authentication ◽  
Keystroke Dynamics ◽  
Continuous Authentication ◽  
Biometric Characteristic ◽  
Continuous User ◽  
Computer Keyboard

This chapter proposes an authentication methodology that is both inexpensive and non-intrusive and authenticates users continuously while using a computer keyboard. This proposed methodology uses neural network committee machines. The committee consists of several independent neural networks trained to recognize a behavioral biometric characteristic: user’s typing pattern. Continuous authentication prevents potential attacks when users leave their desks without logging out or locking their computer session. Some experiments were conducted to evaluate and to calibrate the authentication committee. Best results show that a 0% FAR and a 0.15% FRR can be achieved when different thresholds are used in the system for each user. In this proposed methodology, capture system does not need to concern about typing errors in the text. Another feature of this methodology is that new users can be easily added to the system, with no need to re-train all neural networks involved.

EEG-Based User Authentication Using a Convolutional Neural Network

2019 ◽  
Author(s):  
Ting Yu ◽  
Chun-Shu Wei ◽  
Kuan-Jung Chiang ◽  
Masaki Nakanishi ◽  
Tzyy-Ping Jung
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
User Authentication

scholarly journals Deep Residual Networks for User Authentication via Hand-Object Manipulations

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 2981
Author(s):  
Kanghae Choi ◽  
Hokyoung Ryu ◽  
Jieun Kim
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
User Authentication ◽  
Ecological Validity ◽  
Age Groups ◽  
Rejection Rate ◽  
Object Manipulation ◽  
Measurement Unit ◽  
Validity Data ◽  
Continuous User

With the ubiquity of wearable devices, various behavioural biometrics have been exploited for continuous user authentication during daily activities. However, biometric authentication using complex hand behaviours have not been sufficiently investigated. This paper presents an implicit and continuous user authentication model based on hand-object manipulation behaviour, using a finger-and hand-mounted inertial measurement unit (IMU)-based system and state-of-the-art deep learning models. We employed three convolutional neural network (CNN)-based deep residual networks (ResNets) with multiple depths (i.e., 50, 101, and 152 layers) and two recurrent neural network (RNN)-based long short-term memory (LSTMs): simple and bidirectional. To increase ecological validity, data collection of hand-object manipulation behaviours was based on three different age groups and simple and complex daily object manipulation scenarios. As a result, both the ResNets and LSTMs models acceptably identified users’ hand behaviour patterns, with the best average accuracy of 96.31% and F1-score of 88.08%. Specifically, in the simple hand behaviour authentication scenarios, more layers in residual networks tended to show better performance without showing conventional degradation problems (the ResNet-152 > ResNet-101 > ResNet-50). In a complex hand behaviour scenario, the ResNet models outperformed user authentication compared to the LSTMs. The 152-layered ResNet and bidirectional LSTM showed an average false rejection rate of 8.34% and 16.67% and an equal error rate of 1.62% and 9.95%, respectively.

scholarly journals PassEye: Sniffing Your Password from HTTP Sessions by Deep Neural Network

2020 ◽  
pp. 3-15
Author(s):  
Zhiqing Rui ◽  
Jingzheng Wu ◽  
Yanjie Shao ◽  
Tianyue Luo ◽  
Mutian Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Regular Expression ◽  
User Authentication ◽  
Neural Network Classifier ◽  
Binary Classifier ◽  
Password Authentication ◽  
Attack Model ◽  
Manual Analysis ◽  
Binary Neural Network

AbstractPasswords are the most widely used method for user authentication in HTTP websites. Password sniffing attacks are considered a common way to steal password. However, most existing methods have many deficiencies in versatility and automation, such as manual analysis, keyword matching, regular expression and SniffPass. In this paper, to better describe the problem, we propose a HTTP Sessions Password Sniffing (HSPS) attack model which is more suitable in HTTP environment. Furthermore, we propose PassEye, a novel deep neural networkbased implementation of HSPS attack. PassEye is a binary neural network classifier that learns features from the HTTP sessions and identifies Password Authentication Session (PAS). We collected 979,681 HTTP sessions from the HTTP and HTTPS websites for training the binary classifier. The results show that PassEye is effective in sniffing the passwords with an accuracy of 99.38%. In addition, several measures are provided to prevent HSPS attacks in the end.

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